In the realm of education, the United States has long been regarded as a land of opportunity, where individuals from diverse backgrounds can pursue their dreams through higher education. However, beneath this narrative of equal opportunity lies a complex web of systemic disparities that significantly affect educational outcomes for various demographic groups. One of the critical barometers of these disparities is the Scholastic Assessment Test (SAT), a standardized test widely used in the college admissions process. The SAT serves as a pivotal component in the higher education landscape, influencing not only individual academic trajectories but also broader discussions about access, equity, and educational policy in the United States.
The American education system, while founded on principles of inclusivity and meritocracy, grapples with stark inequalities. These disparities are deeply intertwined with socioeconomic status, geographic location, and racial identity, among other factors. Historically, SAT scores have been an instrument to assess academic preparedness for college, but they have also been criticized for their potential to perpetuate these disparities, particularly among different racial and ethnic groups. This personal project aims to delve into the intricacies of SAT scores, using data collected from the College Board SAT Annual Reports.Through exploratory data analysis the following questions will be addressed:
By analyzing historical data and employing statistical techniques, the objective of this project is to employ data visualization techniques to dissect the SAT results, uncover underlying patterns, and shed light on how these disparities manifest within the American education system.
Data collected from the College Board SAT Suite Annual Reports References: https://www.statista.com/statistics/233324/median-household-income-in-the-united-states-by-race-or-ethnic-group/
# Import the Necessary Libraries
import pandas as pd
pd.options.mode.chained_assignment = None
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
# Import the Exel Files to Be Read
df_2022 = pd.read_excel('2022 SAT Data.xlsx')
df_2021 = pd.read_excel('2021 SAT Data.xlsx')
df_2020 = pd.read_excel('2020 SAT Data.xlsx')
df_2019 = pd.read_excel('2019 SAT Data.xlsx')
df_2018 = pd.read_excel('2018 SAT Data.xlsx')
df_2017 = pd.read_excel('2017 SAT Data.xlsx')
df_2016 = pd.read_excel('2016 SAT Data.xlsx')
# 2022 SAT Data Frame: Race/Ethnicity, Parental Education, Mean Family Income
df_ethnicity_2022 = df_2022.iloc[5:13,:]
df_ethnicity_2022.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_ethnicity_2022['Year']= '2022'
df_parent_education_2022 = df_2022.iloc[27:33,:]
df_parent_education_2022.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_parent_education_2022['Year']= '2022'
df_family_income_2022 = df_2022.iloc[35:40,:]
df_family_income_2022.columns=['Mean Family Income','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_family_income_2022['Year']= '2022'
# 2021 SAT Data Frame: Race/Ethnicity and Parental Education
df_ethnicity_2021 = df_2021.iloc[5:13,:]
df_ethnicity_2021.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_ethnicity_2021['Year']= '2021'
df_parent_education_2021 = df_2021.iloc[26:32,:]
df_parent_education_2021.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_parent_education_2021['Year']= '2021'
# 2020 SAT Data Frame: Race/Ethnicity and Parental Education
df_ethnicity_2020 = df_2020.iloc[5:13,:]
df_ethnicity_2020.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_ethnicity_2020['Year']= '2020'
df_parent_education_2020 = df_2020.iloc[26:32,:]
df_parent_education_2020.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_parent_education_2020['Year']= '2020'
# 2019 SAT Data Frame: Race/Ethnicity and Parental Education
df_ethnicity_2019 = df_2019.iloc[5:13,:]
df_ethnicity_2019.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_ethnicity_2019['Year']= '2019'
df_parent_education_2019 = df_2019.iloc[26:32,:]
df_parent_education_2019.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_parent_education_2019['Year']= '2019'
# 2018 SAT Data Frame: Race/Ethnicity and Parental Education
df_ethnicity_2018 = df_2018.iloc[5:13,:]
df_ethnicity_2018.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_ethnicity_2018['Year']= '2018'
df_parent_education_2018 = df_2018.iloc[26:32,:]
df_parent_education_2018.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_parent_education_2018['Year']= '2018'
# 2017 SAT Data Frame: Race/Ethnicity and Parental Education
df_ethnicity_2017 = df_2017.iloc[5:13,:]
df_ethnicity_2017.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_ethnicity_2017['Year']= '2017'
df_parent_education_2017 = df_2017.iloc[25:31,:]
df_parent_education_2017.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Met Both Benchmarks','Met ERW Benchmark','Met Math Benchmark','Met No Benchmarks']
df_parent_education_2017['Year']= '2017'
# 2016 SAT Data Frame: Race/Ethnicity, Parental Education, Mean Family Income
df_ethnicity_2016 = df_2016.iloc[0:9,:]
df_ethnicity_2016.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean ERW Score','Mean Math Score']
df_ethnicity_2016['Mean Total Score']= df_ethnicity_2016['Mean ERW Score'] + df_ethnicity_2016['Mean Math Score']
df_ethnicity_2016['Year']= '2016'
df_ethnicity_2016 = df_ethnicity_2016[['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Year']]
df_parent_education_2016 = df_2016.iloc[21:27,:]
df_parent_education_2016.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean ERW Score','Mean Math Score']
df_parent_education_2016['Mean Total Score']= df_parent_education_2016['Mean ERW Score'] + df_ethnicity_2016['Mean Math Score']
df_parent_education_2016['Year']= '2016'
df_parent_education_2016 = df_ethnicity_2016[['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Year']]
df_family_income_2016 = df_2016.iloc[11:20,:]
df_family_income_2016.columns=['Race/Ethnicity','Number of Test Takers','Percent','Mean ERW Score','Mean Math Score']
df_family_income_2016['Mean Total Score']= df_family_income_2016['Mean ERW Score'] + df_family_income_2016['Mean Math Score']
df_family_income_2016['Year']= '2016'
df_family_income_2016 = df_family_income_2016[['Race/Ethnicity','Number of Test Takers','Percent','Mean Total Score','Mean ERW Score','Mean Math Score','Year']]
# Data Frame for the 2021 U.S Median Household Income
US_medianincome2021 = { 'Race/Ethnicity': ['American Indian and Alaska Native', 'Asian','Black/African American', 'Hispanic/Latino','White'],
'Median Household Income': ['49216','101418','45208','57981','71033']}
df_US_US_medianincome2021= pd.DataFrame(US_medianincome2021)
# Combine Data Frames
df_ethnicity = pd.concat([df_ethnicity_2022,df_ethnicity_2021,df_ethnicity_2020,df_ethnicity_2019,df_ethnicity_2018,df_ethnicity_2017,df_ethnicity_2016],ignore_index=True)
df_parent_education = pd.concat([df_parent_education_2022,df_parent_education_2021,df_parent_education_2020,df_parent_education_2019,df_parent_education_2018,df_parent_education_2017,df_parent_education_2016],ignore_index=True)
df_ethnicity
| Race/Ethnicity | Number of Test Takers | Percent | Mean Total Score | Mean ERW Score | Mean Math Score | Met Both Benchmarks | Met ERW Benchmark | Met Math Benchmark | Met No Benchmarks | Year | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | American Indian/Alaska Native | 14,800 | 1% | 936 | 473 | 463 | 22% | 44% | 24% | 54% | 2022 |
| 1 | Asian | 175,468 | 10% | 1229 | 596 | 633 | 75% | 84% | 80% | 11% | 2022 |
| 2 | Black/African American | 201,645 | 12% | 926 | 474 | 452 | 19% | 44% | 21% | 54% | 2022 |
| 3 | Hispanic/Latino | 396,422 | 23% | 964 | 491 | 473 | 26% | 52% | 28% | 47% | 2022 |
| 4 | Native Hawaiian/Other Pacific Islander | 3,376 | 0% | 945 | 481 | 464 | 24% | 47% | 26% | 51% | 2022 |
| 5 | White | 732,946 | 42% | 1098 | 556 | 543 | 53% | 77% | 55% | 21% | 2022 |
| 6 | Two or More Races | 66,702 | 4% | 1102 | 559 | 543 | 52% | 77% | 54% | 22% | 2022 |
| 7 | No Response | 146,319 | 8% | 983 | 489 | 494 | 31% | 47% | 35% | 49% | 2022 |
| 8 | American Indian/Alaska Native | 10,288 | 1% | 927 | 468 | 459 | 21% | 42% | 24% | 56% | 2021 |
| 9 | Asian | 167,208 | 11% | 1239 | 597 | 642 | 78% | 85% | 83% | 10% | 2021 |
| 10 | Black/African American | 168,454 | 11% | 934 | 477 | 457 | 22% | 46% | 23% | 53% | 2021 |
| 11 | Hispanic/Latino | 352,094 | 23% | 967 | 490 | 477 | 28% | 52% | 30% | 46% | 2021 |
| 12 | Native Hawaiian/Other Pacific Islander | 3,015 | 0% | 950 | 481 | 469 | 26% | 48% | 28% | 50% | 2021 |
| 13 | White | 635,486 | 42% | 1112 | 562 | 550 | 57% | 80% | 59% | 18% | 2021 |
| 14 | Two or More Races | 54,961 | 4% | 1116 | 565 | 551 | 56% | 79% | 58% | 20% | 2021 |
| 15 | No Response | 117,627 | 8% | 976 | 483 | 493 | 31% | 46% | 36% | 49% | 2021 |
| 16 | American Indian/Alaska Native | 14,050 | 1% | 902 | 456 | 447 | 17% | 38% | 20% | 60% | 2020 |
| 17 | Asian | 223,451 | 10% | 1217 | 585 | 632 | 74% | 83% | 80% | 11% | 2020 |
| 18 | Black/African American | 261,326 | 12% | 927 | 473 | 454 | 20% | 44% | 21% | 54% | 2020 |
| 19 | Hispanic/Latino | 569,370 | 26% | 969 | 491 | 478 | 28% | 53% | 30% | 45% | 2020 |
| 20 | Native Hawaiian/Other Pacific Islander | 5,107 | 0% | 948 | 478 | 470 | 24% | 47% | 27% | 50% | 2020 |
| 21 | White | 909,987 | 41% | 1104 | 557 | 547 | 56% | 79% | 59% | 19% | 2020 |
| 22 | Two or More Races | 89,656 | 4% | 1091 | 552 | 539 | 52% | 76% | 53% | 22% | 2020 |
| 23 | No Response | 125,513 | 6% | 996 | 488 | 507 | 36% | 49% | 41% | 45% | 2020 |
| 24 | American Indian/Alaska Native | 12,917 | 1% | 912 | 461 | 451 | 18% | 39% | 21% | 58% | 2019 |
| 25 | Asian | 228,527 | 10% | 1223 | 586 | 637 | 75% | 83% | 80% | 11% | 2019 |
| 26 | Black/African American | 271,178 | 12% | 933 | 476 | 457 | 20% | 46% | 22% | 53% | 2019 |
| 27 | Hispanic/Latino | 554,665 | 25% | 978 | 495 | 483 | 29% | 55% | 31% | 43% | 2019 |
| 28 | Native Hawaiian/Other Pacific Islander | 5,430 | 0% | 964 | 487 | 478 | 27% | 51% | 29% | 47% | 2019 |
| 29 | White | 947,842 | 43% | 1114 | 562 | 553 | 57% | 80% | 59% | 18% | 2019 |
| 30 | Two or More Races | 87,178 | 4% | 1095 | 554 | 540 | 51% | 76% | 53% | 22% | 2019 |
| 31 | No Response | 112,350 | 5% | 959 | 472 | 487 | 28% | 44% | 34% | 50% | 2019 |
| 32 | American Indian/Alaska Native | 10,946 | 1% | 949 | 480 | 469 | 24% | 48% | 26% | 50% | 2018 |
| 33 | Asian | 217,971 | 10% | 1223 | 588 | 635 | 75% | 85% | 81% | 10% | 2018 |
| 34 | Black/African American | 263,318 | 12% | 946 | 483 | 463 | 21% | 50% | 23% | 49% | 2018 |
| 35 | Hispanic/Latino | 499,442 | 23% | 990 | 501 | 489 | 31% | 58% | 33% | 40% | 2018 |
| 36 | Native Hawaiian/Other Pacific Islander | 5,620 | 0% | 986 | 498 | 489 | 31% | 57% | 33% | 40% | 2018 |
| 37 | White | 930,825 | 44% | 1123 | 566 | 557 | 59% | 82% | 61% | 16% | 2018 |
| 38 | Two or More Races | 77,078 | 4% | 1101 | 558 | 543 | 52% | 78% | 54% | 20% | 2018 |
| 39 | No Response | 131,339 | 6% | 954 | 472 | 481 | 26% | 44% | 31% | 51% | 2018 |
| 40 | American Indian/Alaska Native | 7,782 | 0% | 963 | 486 | 477 | 27% | 53% | 29% | 45% | 2017 |
| 41 | Asian | 158,031 | 9% | 1181 | 569 | 612 | 70% | 81% | 76% | 12% | 2017 |
| 42 | Black/African American | 225,860 | 13% | 941 | 479 | 462 | 20% | 49% | 22% | 50% | 2017 |
| 43 | Hispanic/Latino | 408,067 | 24% | 990 | 500 | 489 | 31% | 58% | 33% | 39% | 2017 |
| 44 | Native Hawaiian/Other Pacific Islander | 4,131 | 0% | 986 | 498 | 488 | 32% | 57% | 34% | 40% | 2017 |
| 45 | White | 760,362 | 44% | 1118 | 565 | 553 | 59% | 83% | 61% | 15% | 2017 |
| 46 | Two or More Races | 57,049 | 3% | 1103 | 560 | 544 | 54% | 80% | 56% | 18% | 2017 |
| 47 | No Response | 94,199 | 5% | 961 | 475 | 485 | 27% | 48% | 33% | 47% | 2017 |
| 48 | American Indian/Alaska Native | 7778.0 | 0.0 | 939.0 | 468.0 | 471.0 | NaN | NaN | NaN | NaN | 2016 |
| 49 | Asian | 196735.0 | 12.0 | 1131.0 | 529.0 | 602.0 | NaN | NaN | NaN | NaN | 2016 |
| 50 | Black/African American | 199306.0 | 12.0 | 855.0 | 430.0 | 425.0 | NaN | NaN | NaN | NaN | 2016 |
| 51 | Native Hawaiian/Other Pacific Islander | 2371.0 | 0.0 | 870.0 | 432.0 | 438.0 | NaN | NaN | NaN | NaN | 2016 |
| 52 | Hispanic/Latino | 355829.0 | 22.0 | 901.0 | 448.0 | 453.0 | NaN | NaN | NaN | NaN | 2016 |
| 53 | White | 742436.0 | 45.0 | 1061.0 | 528.0 | 533.0 | NaN | NaN | NaN | NaN | 2016 |
| 54 | Two or More Races | 28460.0 | 2.0 | 1016.0 | 511.0 | 505.0 | NaN | NaN | NaN | NaN | 2016 |
| 55 | Other | 20604.0 | 1.0 | 1015.0 | 496.0 | 519.0 | NaN | NaN | NaN | NaN | 2016 |
| 56 | No Response | 84070.0 | 5.0 | 952.0 | 451.0 | 501.0 | NaN | NaN | NaN | NaN | 2016 |
fig = px.line(df_ethnicity, x='Year', y='Mean Total Score',color='Race/Ethnicity', markers=True)
fig.update_layout(autotypenumbers='convert types',
title_text = 'Examination of 2016-2022 SAT Scores and Racial/Ethnic Factors')
fig.show()
An examination of the mean total SAT scores over the last seven years reveals a consistent pattern: Asians consistently achieve the highest scores, followed by Whites, while Black/African Americans and American Indian/Alaska Natives consistently score lower.
# Exploring the Influence of Race and Ethnicity on SAT Scores: A Comparative Analysis
fig_score_by_race = px.line(df_ethnicity_2022, x='Race/Ethnicity', y = ['Mean Total Score'], markers=True)
fig_score_by_race.update_layout(autotypenumbers='convert types', # Updates the values from the dataframe from type object to numeric values
title_text = 'Examination of 2022 SAT Scores and Racial/Ethnic Factors', #Title of the Plot
xaxis_title = 'Race/Ethnicity', #x-axis label
legend_title = 'Legend',
yaxis_title = 'SAT Scores') #y-axis label # Updates the values from the dataframe from type object to numeric values
fig_score_by_race.show()
Analyzing mean total SAT score data only from 2022, the disparities between races are even more apparent. Asians achieved the highest mean score at 1229, falling within the 75-81 percentile range, while Whites followed as the second-highest scoring group with a mean score of 1098, within the 51-61 percentile range. On the other hand, Black/African Americans and American Indian/Alaska Natives scored significantly lower, with mean scores of 926 and 936, respectively, falling within the 27-35 percentile range.
# Exploring the Influence of Race and Ethnicity on ERW and Math SAT Scores: A Comparative Analysis
fig_score_by_race = px.line(df_ethnicity_2022, x='Race/Ethnicity', y = ['Mean ERW Score','Mean Math Score'], markers=True)
fig_score_by_race.update_layout(autotypenumbers='convert types', # Updates the values from the dataframe from type object to numeric values
title_text = 'Examination of 2022 SAT ERW and Math Scores and Racial/Ethnic Factors', #Title of the Plot
xaxis_title = 'Race/Ethnicity', #x-axis label
legend_title = 'Legend',
yaxis_title = 'SAT Scores') #y-axis label # Updates the values from the dataframe from type object to numeric values
fig_score_by_race.update_traces(textposition='top center')
fig_score_by_race.show()
fig_gender_bar = px.histogram(df_ethnicity_2022,x='Race/Ethnicity',y = ['Mean Total Score','Mean ERW Score','Mean Math Score'],text_auto=True,barmode='group')
fig_gender_bar.update_layout(title_text = 'Exploring the Influence of Race and Ethnicity on 2022 SAT Scores', #Title of the Plot
legend_title = 'Legend',
xaxis_title = 'Race/Ethnicity', #x-axis label
yaxis_title = 'SAT Scores', #y-axis label
barmode='group',
bargap=0.01, #Gap between bars of adjacent location
bargroupgap=0.01) #Gap between bars of the same location coordinates
fig_gender_bar.show()
In 2022, an intriguing finding emerged when examining mean SAT scores for the Evidence-Based Reading and Writing (ERW) and Math sections among different racial and ethnic groups. Asians were the only group to score higher in Math than in ERW, suggesting a particular strength in mathematical aptitude. Conversely, for all other racial groups, including Whites, Blacks/African Americans, Hispanics/Latinos and American Indian/Alaska Natives, the scores in ERW were higher than those in Math. Notably, Black/African Americans recorded the lowest mean scores across both sections, with an average of 452 in math and 474 in reading and writing, underlining the ongoing challenges in addressing educational disparities.
# Analysis of Influence of Median Family Income on SAT Scores
fig_race_line = px.line(df_family_income_2022, x='Mean Family Income', y = ['Mean Total Score'], markers=True)
fig_race_line.update_layout(autotypenumbers='convert types', # Updates the values from the dataframe from type object to numeric values
title_text = 'Exploring the Influence of Median Family Income on 2022 SAT Scores', #Title of the Plot
xaxis_title = 'Median Family Income', #x-axis label
legend_title = 'Legend',
yaxis_title = 'SAT Scores') #y-axis label # Updates the values from the dataframe from type object to numeric values
fig_race_line.show()
The analysis of the influence of median family income on 2022 SAT scores reveals a robust positive correlation, indicating that as median family income increases, so do the mean total SAT scores. This strong correlation underscores the role of socioeconomic factors in educational achievement, highlighting the advantages that come with higher family income levels.
fig_gender_bar = px.histogram(df_US_US_medianincome2021,x='Race/Ethnicity',y = ['Median Household Income'],text_auto=True,barmode='group')
fig_gender_bar.update_layout(title_text = 'Median Household Income in the United States in 2021 by Race/Ethnicity', #Title of the Plot
legend_title = 'Legend',
xaxis_title = 'Year', #x-axis label
yaxis_title = 'Median Household Income', #y-axis label
barmode='group',
bargap=0.01, #Gap between bars of adjacent location
bargroupgap=0.01) #Gap between bars of the same location coordinates
fig_gender_bar.show()
The 2021 median household income data by race/ethnicity further solidifies the link between socioeconomic factors and educational outcomes. Asians ranked the highest with a median family income of \$101,418, aligning with their consistently high SAT scores. In contrast, Black/African Americans, who had the lowest median family income at \\$45,208, are among the racial groups with the lowest SAT scores, demonstrating the profound impact of economic disparities on educational achievements.
The analysis of SAT scores across racial and ethnic groups in the United States reveals consistent disparities. Asians consistently score highest, followed by Whites, while Black/African Americans and American Indian/Alaska Natives consistently score lower, indicating that race and ethnicity significantly influence SAT performance. Over the past decade, these disparities have shown little change, highlighting persistent systemic challenges. Family income strongly correlates with SAT performance, with higher incomes associated with higher scores. This income disparity underscores the role of socioeconomic status in educational outcomes and raises concerns about unequal access to test preparation resources. These findings have implications for college admissions, as students from racial groups with lower average scores may face barriers, emphasizing the need for holistic admissions criteria and comprehensive efforts to promote educational equity.
In conclusion, this report underscores the need for systemic solutions to address disparities in the American education system. Initiatives focusing on equitable access to quality education, increased investment in underserved communities, culturally responsive teaching practices, and affordable test preparation resources are vital steps toward achieving educational equity. The college admissions process must also consider these disparities and adapt to create a more inclusive and fair environment for all students.